Text Categorization Models for Retrieval of High Quality Articles in Internal Medicine
Identifieur interne : 000111 ( Pmc/Curation ); précédent : 000110; suivant : 000112Text Categorization Models for Retrieval of High Quality Articles in Internal Medicine
Auteurs : Y. Aphinyanaphongs ; C. F. AliferisSource :
- AMIA Annual Symposium Proceedings [ 1942-597X ] ; 2003.
Abstract
The discipline of Evidence Based Medicine (EBM) studies formal and quasi-formal methods for identifying high quality medical information and abstracting it in useful forms so that patients receive the best customized care possible [
Url:
PubMed: 14728128
PubMed Central: 1480096
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<author><name sortKey="Aphinyanaphongs, Y" sort="Aphinyanaphongs, Y" uniqKey="Aphinyanaphongs Y" first="Y." last="Aphinyanaphongs">Y. Aphinyanaphongs</name>
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<author><name sortKey="Aliferis, C F" sort="Aliferis, C F" uniqKey="Aliferis C" first="C. F." last="Aliferis">C. F. Aliferis</name>
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<front><div type="abstract" xml:lang="en"><p>The discipline of Evidence Based Medicine (EBM) studies formal and quasi-formal methods for identifying high quality medical information and abstracting it in useful forms so that patients receive the best customized care possible [<xref ref-type="bibr" rid="b1-007">1</xref>
]. Current computer-based methods for finding high quality information in PubMed and similar bibliographic resources utilize search tools that employ preconstructed Boolean queries. These clinical queries are derived from a combined application of (a) user interviews, (b) ad-hoc manual document quality review, and (c) search over a constrained space of disjunctive Boolean queries. The present research explores the use of powerful text categorization (machine learning) methods to identify content-specific and high-quality PubMed articles. Our results show that models built with the proposed approach outperform the Boolean based PubMed clinical query filters in discriminatory power.</p>
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<pmc article-type="research-article"><pmc-comment>The publisher of this article does not allow downloading of the full text in XML form.</pmc-comment>
<front><journal-meta><journal-id journal-id-type="nlm-ta">AMIA Annu Symp Proc</journal-id>
<journal-title-group><journal-title>AMIA Annual Symposium Proceedings</journal-title>
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<issn pub-type="epub">1942-597X</issn>
<publisher><publisher-name>American Medical Informatics Association</publisher-name>
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<article-meta><article-id pub-id-type="pmid">14728128</article-id>
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<article-id pub-id-type="publisher-id">007</article-id>
<article-categories><subj-group subj-group-type="heading"><subject>Article</subject>
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<title-group><article-title>Text Categorization Models for Retrieval of High Quality Articles in Internal Medicine</article-title>
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<contrib-group><contrib contrib-type="author"><name><surname>Aphinyanaphongs</surname>
<given-names>Y.</given-names>
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<degrees>M.S.</degrees>
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<contrib contrib-type="author"><name><surname>Aliferis</surname>
<given-names>C.F.</given-names>
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<degrees>M.D., Ph.D.</degrees>
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<aff id="af1-007">Department of Biomedical Informatics, Vanderbilt University, Nashville, TN</aff>
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<pub-date pub-type="ppub"><year>2003</year>
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<volume>2003</volume>
<fpage>31</fpage>
<lpage>35</lpage>
<permissions><copyright-statement>This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose.</copyright-statement>
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<abstract><p>The discipline of Evidence Based Medicine (EBM) studies formal and quasi-formal methods for identifying high quality medical information and abstracting it in useful forms so that patients receive the best customized care possible [<xref ref-type="bibr" rid="b1-007">1</xref>
]. Current computer-based methods for finding high quality information in PubMed and similar bibliographic resources utilize search tools that employ preconstructed Boolean queries. These clinical queries are derived from a combined application of (a) user interviews, (b) ad-hoc manual document quality review, and (c) search over a constrained space of disjunctive Boolean queries. The present research explores the use of powerful text categorization (machine learning) methods to identify content-specific and high-quality PubMed articles. Our results show that models built with the proposed approach outperform the Boolean based PubMed clinical query filters in discriminatory power.</p>
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